Full-Stack Developer

Odaia Intelligence Inc.•Toronto, ON

1d•CA$90,000 - CA$120,000•Remote

About The Position

ODAIA delivers AI-powered commercial intelligence for life sciences - unifying data, accelerating results, and helping commercial teams deepen engagement to enhance care for providers and patients. ODAIA's AI platform transforms complex data into predictive, personalized insights, enabling commercial leaders to understand their customers, anticipate prescribing behaviors, and make the informed, strategic decisions that bring therapies to patients faster. OUR MISSION: Reducing patients' time to therapy by facilitating meaningful interactions with healthcare providers, through human-centric software powered by AI. We're looking for a Full-Stack Developer to join our engineering team! You'll work across the full stack to build and ship features that directly impact how pharma commercial teams engage with healthcare providers. You'll work closely with senior engineers, product, and design to turn well-scoped requirements into shipped, working software, while growing your ownership over time.

Requirements

3 + years of professional software development experience, including meaningful ownership across frontend and backend systems
Strong experience with React and TypeScript, along with a solid understanding of browser fundamentals, state management, component design, accessibility, and frontend performance
Experience designing, building, and operating APIs or backend services using Python, TypeScript or a comparable technology.
Experience integrating third-party APIs and designing resilient behaviour around authentication, rate limits, timeouts, retries, idempotency, partial failures, and changing external contracts.
Strong software engineering fundamentals, including modular design, clear interfaces, appropriate abstractions, automated testing, source control, and maintainable code.
A working understanding of distributed systems concepts, including concurrency, caching, queues, asynchronous processing, failure handling, and performance trade-offs
The ability to guide coding AI agents, evaluating output, catching bad assumptions, reviewing multi-file changes, and owning the final results
Clear written and verbal communication, including the confidence to surface ambiguity, ask useful questions, challenge assumptions constructively, and explain technical trade-offs to different audiences.
Comfort working in an evolving environment where priorities can change as customer, product, and technical information becomes clearer.
Curiosity, low ego, and a demonstrated ability to seek feedback, learn unfamiliar systems, and improve both your work and the way the team operates.

Responsibilities

Own well-defined product capabilities end-to-end, from understanding the user problem and shaping the technical approach through implementation, testing, deployment, monitoring, and iteration
Build accessible, responsive user experiences with React and TypeScript, including data-rich workflows that make complex commercial and healthcare insights clear and actionable
Build expertise in ODAIA’s life sciences domain and increasingly contribute engineering insight to product and technical planning
Use Claude Code and other coding AI agents throughout the software development lifecycle for exploration, implementation, testing, debugging, documentation, code review, and refactoring
Design and maintain reliable APIs and backend services that connect ODAIA’s data, machine learning, analytics, and agentic capabilities to customer-facing products
Experience with AWS and Kubernetes, with confidence building, containerizing, deploying, and operating applications using Docker or equivalent container technologies
Work across application and data systems, including Snowflake, PostgreSQL, analytical datasets, third-party APIs, asynchronous workflows, and embedded analytical engines such as DuckDB and DataFusion
Collaborate with product, design, and ML engineering to turn models and data products into useful, trustworthy customer experiences rather than stopping at technical integration
Use instrumentation, logs, metrics, traces, and product analytics to understand how features behave in production and identify where they should be improved
Ship incrementally using automated testing, continuous integration, feature flags, progressive delivery, and clearly defined success
Participate actively in technical design, estimation, code review, incident learning, and architectural discussions, raising risks, ambiguity, and trade-offs early.